In the medical industry, there is often a need for a laboratory technician, e.g., a cytotechnologist, to review a cytological specimen for the presence of specified cell types. For example, there is presently a need to review a cervico-vaginal Papanicolaou (Pap) smear slides for the presence of malignant or pre-malignant cells. Since its introduction over fifty years ago, Pap smears have been a powerful tool for detecting cancerous and precancerous cervical lesions. During that time, the Pap smear has been credited with reducing mortality from cervical cancer by as much as 70%. This once precipitous drop in the death rate has slowed however, and the mortality rate in the United States for this preventable disease has remained virtually constant, at about 5,000 per year since the mid-eighties. Therefore, about one-third of the 15,000 women diagnosed with cervical cancer annually still die, because the cancer was detected too late. A further cause for concern is National Cancer Institute data that shows an annual 3% increase in the incidence of invasive cervical cancer in white women under 50 years of age since 1986.
A number of factors may be contributing to this current threshold, not the least of which is the fact that many women, particularly in high risk populations, are still not participating in routine cervical cancer screening. Another contributing factor that has received much attention is the limitation of the traditional Pap smear method itself. The reliability and efficacy of a cervical screening method is measured by its ability to diagnose precancerous lesions (sensitivity) while at the same time avoiding false positive diagnosis (specificity). In turn, these criteria are dependent on the accuracy of the cytological interpretation. The conventional Pap smear has false negative rates ranging from 10-50%. This is due in large part to the vast number of cells and objects (typically as many as 100,000 to 200,000) that must be reviewed by a technician to determine the possible existence of a small number of malignant or pre-malignant cells. Thus, Pap smear tests, as well as other tests requiring detailed review of biological material, have suffered from a high false negative rate due to fatigue imposed on the technician.
To facilitate this review process, automated systems have been developed by various companies to focus the technician's attention on the most pertinent cells, with a potential to discard the remaining cells from further review. A typical automated system includes an imager and an automated optical microscope. Briefly, the imager can be operated to provide a series of numerous images of a cytological specimen slide, referred to as image frames, each depicting a different portion of the slide. The imager then processes these image frames to determine the most pertinent biological objects or regions of interest for review on the slide, and their locations (x-y coordinates) on the slide. For example, a region of interest may include a cellular organelle or cell structure such as a stained cell nucleus. This information is then passed on to the microscope, which, based on the x-y coordinates received from the imager, automatically or semi-automatically displays the biological objects for review by the technician.
In the process of determining the most pertinent biological objects, the automated system must perform segmentation and feature extraction on all cells present within each frame. Image segmentation involves a process of creating a digital representation of all the pixels in an image that represent a particular structure, such as, for instance, a cell nucleus. In order to perform image segmentation, there is a need to identify the boundaries or edges between various organelles or structures located within the cytological image data. For instance, image segmentation of the nucleus first requires the identification of the edges or boundary between the nucleus and the cell cytoplasm. Similarly, image segmentation of the whole cells requires establishing the edge or boundary between the cell cytoplasm and the background. Feature extraction involves the measurement of one or more parameters about a particular organelle or cellular structure. Certain classes of image features require that one identify the edges or boundaries between the various cellular components (e.g., cytoplasm, nucleus, nucleoli, etc.). In the case of applications to Pap smear images, the optical density of the cell nucleus or the nucleus-to-cytoplasm ratio may be used to identify potential diseased states of the tissue.
The spatial gradient is a useful measure of edges in image data, and within the field of cytological image processing, the spatial gradient can be used for both segmentation and feature extraction. However, in the case of segmentation, the spatial gradient is of limited use because the gradient captures edges corresponding to borders or edges between the nuclear material and the cytoplasm as well as the border formed between the cytoplasm and the background. Because cytoplasm edges may be present, the gradient information must be used cautiously, if at all, by segmentation algorithms because the cytoplasm edge is often much stronger or more dominant than nuclear edges, and thus can lead to over-segmentation of the cell nucleus.